Description
Some commonly employed statistical tests are the independent-samples t-test, paired-samples t-test, and One-Way ANOVA. In this assignment, you will practice conducting independent-samples t-tests, paired-samples t-tests, and One-Way ANOVAs from an SPSS data set.
General Requirements:
Use the following information to ensure successful completion of the assignment:
- Review “SPSS Access Instructions” for information on how to access SPSS for this assignment.
- Access the document, “Introduction to Statistical Analysis Using IBM SPSS Statistics, Student Guide” to complete the assignment.
- Download the file “Census.sav” and open it with SPSS. Use the data to complete the assignment.
- Download the file “SPSS_CUST.sav” and open it with SPSS. Use the data to complete the assignment.
Directions:
- Locate the data set “Census.sav” and open it with SPSS. Follow the steps in section 7.14 Learning Activity as written. Answer questions 1-3 in the activity based on your observations of the SPSS output.Type your answers into a Word document. Copy and paste the full SPSS output including any supporting graphs and tables directly from SPSS into the Word document for submission to the instructor. The SPSS output must be submitted with the problem set answers in order to receive full credit for the assignment.
- Locate the data set “SPSS_CUST.sav” and open it with SPSS. Follow the steps in section 8.10 Learning Activity as written. Answer all of the questions in the activity based on your observations of the SPSS output. Type your answers into a Word document. Copy and paste the full SPSS output including any supporting graphs and tables directly from SPSS into the Word document for submission to the instructor. The SPSS output must be submitted with the problem set answers in order to receive full credit for the assignment.
- Locate the data set “Census.sav” and open it with SPSS. Follow the steps in section 9.20 Learning Activity as written. Answer questions 1-3 in the activity based on your observations of the SPSS output. Type your answers into a Word document. Copy and paste the full SPSS output including any supporting graphs and tables directly from SPSS into the Word document for submission to the instructor. The SPSS output must be submitted with the problem set answers in order to receive full credit for the assignment.
Introduction to Statistical
Analysis Using IBM SPSS
Statistics
Student Guide
Course Code: 0G517
ERC 1.0
Introduction to Statistical Analysis Using IBM
SPSS Statistics
Licensed Materials – Property of IBM
© Copyright IBM Corp. 2010
0G517
Published October 2010
US Government Users Restricted Rights – Use,
duplication or disclosure restricted by GSA ADP
Schedule Contract with IBM Corp.
IBM, the IBM logo and ibm.com are trademarks
of International Business Machines Corp.,
registered in many jurisdictions worldwide.
SPSS, SamplePower, and PASW are trademarks
of SPSS Inc., an IBM Company, registered in
many jurisdictions worldwide.
Other product and service names might be
trademarks of IBM or other companies.
This guide contains proprietary information which
is protected by copyright. No part of this
document may be photocopied, reproduced, or
translated into another language without a legal
license agreement from IBM Corporation.
Any references in this information to non-IBM
Web sites are provided for convenience only and
do not in any manner serve as an endorsement
of those Web sites. The materials at those Web
sites are not part of the materials for this IBM
product and use of those Web sites is at your
own risk.
TABLE OF CONTENTS
Table of Contents
LESSON 0: COURSE INTRODUCTION ………………………………………….. 0-1
0.1 INTRODUCTION ……………………………………………………………………………………………………. 0-1
0.2 COURSE OBJECTIVES ……………………………………………………………………………………………. 0-1
0.3 ABOUT SPSS ………………………………………………………………………………………………………. 0-1
0.4 SUPPORTING MATERIALS ……………………………………………………………………………………… 0-2
0.5 COURSE ASSUMPTIONS ………………………………………………………………………………………… 0-2
LESSON 1: INTRODUCTION TO STATISTICAL ANALYSIS …………. 1-1
1.1 OBJECTIVES ………………………………………………………………………………………………………… 1-1
1.2 INTRODUCTION ……………………………………………………………………………………………………. 1-1
1.3 BASIC STEPS OF THE RESEARCH PROCESS ………………………………………………………………. 1-1
1.4 POPULATIONS AND SAMPLES ………………………………………………………………………………… 1-3
1.5 RESEARCH DESIGN ………………………………………………………………………………………………. 1-3
1.6 INDEPENDENT AND DEPENDENT VARIABLES …………………………………………………………… 1-4
1.7 NOTE ABOUT DEFAULT STARTUP FOLDER AND VARIABLE DISPLAY IN DIALOG BOXES .. 1-4
1.8 LESSON SUMMARY ………………………………………………………………………………………………. 1-5
1.9 LEARNING ACTIVITY ……………………………………………………………………………………………. 1-6
LESSON 2: UNDERSTANDING DATA DISTRIBUTIONS – THEORY2-1
2.1 OBJECTIVES ………………………………………………………………………………………………………… 2-1
INTRODUCTION …………………………………………………………………………………………………………. 2-1
2.2 LEVELS OF MEASUREMENT AND STATISTICAL METHODS …………………………………………. 2-1
2.3 MEASURES OF CENTRAL TENDENCY AND DISPERSION …………………………………………….. 2-5
2.4 NORMAL DISTRIBUTIONS ……………………………………………………………………………………… 2-7
2.5 STANDARDIZED (Z-) SCORES ………………………………………………………………………………… 2-8
2.6 REQUESTING STANDARDIZED (Z-) SCORES……………………………………………………………. 2-10
2.7 STANDARDIZED (Z-) SCORES OUTPUT ………………………………………………………………….. 2-10
2.8 PROCEDURE: DESCRIPTIVES FOR STANDARDIZED (Z-) SCORES ……………………………….. 2-10
2.9 DEMONSTRATION: DESCRIPTIVES FOR Z-SCORES…………………………………………………… 2-11
2.10 LESSON SUMMARY …………………………………………………………………………………………… 2-12
2.11 LEARNING ACTIVITY ………………………………………………………………………………………… 2-13
LESSON 3: DATA DISTRIBUTIONS FOR CATEGORICAL
VARIABLES ……………………………………………………………………………….. 3-1
3.1 OBJECTIVES ………………………………………………………………………………………………………… 3-1
3.2 INTRODUCTION ……………………………………………………………………………………………………. 3-1
3.3 USING FREQUENCIES TO SUMMARIZE NOMINAL AND ORDINAL VARIABLES ……………….. 3-2
3.4 REQUESTING FREQUENCIES ………………………………………………………………………………….. 3-3
3.5 FREQUENCIES OUTPUT …………………………………………………………………………………………. 3-3
3.6 PROCEDURE: FREQUENCIES ………………………………………………………………………………….. 3-4
3.7 DEMONSTRATION: FREQUENCIES …………………………………………………………………………… 3-6
3.8 LESSON SUMMARY …………………………………………………………………………………………….. 3-10
3.9 LEARNING ACTIVITY ………………………………………………………………………………………….. 3-10
i
INTRODUCTION TO STATISTICAL ANALYSIS USING IBM SPSS STATISTICS
LESSON 4: DATA DISTRIBUTIONS FOR SCALE VARIABLES ……… 4-1
4.1 OBJECTIVES …………………………………………………………………………………………………………4-1
4.2 INTRODUCTION …………………………………………………………………………………………………….4-1
4.3 SUMMARIZING SCALE VARIABLES USING FREQUENCIES……………………………………………4-1
4.4 REQUESTING FREQUENCIES ……………………………………………………………………………………4-2
4.5 FREQUENCIES OUTPUT ………………………………………………………………………………………….4-2
4.6 PROCEDURE: FREQUENCIES ……………………………………………………………………………………4-4
4.7 DEMONSTRATION: FREQUENCIES ……………………………………………………………………………4-6
4.8 SUMMARIZING SCALE VARIABLES USING DESCRIPTIVES………………………………………….4-11
4.9 REQUESTING DESCRIPTIVES …………………………………………………………………………………4-11
4.10 DESCRIPTIVES OUTPUT ………………………………………………………………………………………4-11
4.11 PROCEDURE: DESCRIPTIVES ……………………………………………………………………………….4-11
4.12 DEMONSTRATION: DESCRIPTIVES………………………………………………………………………..4-12
4.13 SUMMARIZING SCALE VARIABLES USING THE EXPLORE PROCEDURE ………………………4-13
4.14 REQUESTING EXPLORE ………………………………………………………………………………………4-13
4.15 PROCEDURE: EXPLORE ………………………………………………………………………………………4-16
4.16 DEMONSTRATION: EXPLORE……………………………………………………………………………….4-19
4.17 LESSON SUMMARY ……………………………………………………………………………………………4-24
4.18 LEARNING ACTIVITY …………………………………………………………………………………………4-25
LESSON 5: MAKING INFERENCES ABOUT POPULATIONS FROM
SAMPLES
……………………………………………………………………………….. 5-1
5.1 OBJECTIVES …………………………………………………………………………………………………………5-1
5.2 INTRODUCTION …………………………………………………………………………………………………….5-1
5.3 BASICS OF MAKING INFERENCES ABOUT POPULATIONS FROM SAMPLES ……………………..5-1
5.4 INFLUENCE OF SAMPLE SIZE …………………………………………………………………………………..5-2
5.5 HYPOTHESIS TESTING ………………………………………………………………………………………….5-10
5.6 THE NATURE OF PROBABILITY ……………………………………………………………………………..5-11
5.7 TYPES OF STATISTICAL ERRORS ……………………………………………………………………………5-11
5.8 STATISTICAL SIGNIFICANCE AND PRACTICAL IMPORTANCE ……………………………………..5-12
5.9 LESSON SUMMARY ……………………………………………………………………………………………..5-13
5.10 LEARNING ACTIVITY …………………………………………………………………………………………5-13
LESSON 6: RELATIONSHIPS BETWEEN CATEGORICAL
VARIABLES ………………………………………………………………………………. 6-1
ii
TABLE OF CONTENTS
6.1 OBJECTIVES ………………………………………………………………………………………………………… 6-1
6.2 INTRODUCTION ……………………………………………………………………………………………………. 6-1
6.3 CROSSTABS…………………………………………………………………………………………………………. 6-2
6.4 CROSSTABS ASSUMPTIONS……………………………………………………………………………………. 6-3
6.5 REQUESTING CROSSTABS ……………………………………………………………………………………… 6-3
6.6 CROSSTABS OUTPUT ……………………………………………………………………………………………. 6-3
6.7 PROCEDURE: CROSSTABS ……………………………………………………………………………………… 6-4
6.8 EXAMPLE: CROSSTABS …………………………………………………………………………………………. 6-5
6.9 CHI-SQUARE TEST ……………………………………………………………………………………………….. 6-7
6.10 REQUESTING THE CHI-SQUARE TEST ……………………………………………………………………. 6-8
6.11 CHI-SQUARE OUTPUT…………………………………………………………………………………………. 6-8
6.12 PROCEDURE: CHI-SQUARE TEST ………………………………………………………………………….. 6-9
6.13 EXAMPLE: CHI-SQUARE TEST ……………………………………………………………………………. 6-10
6.14 CLUSTERED BAR CHART …………………………………………………………………………………… 6-11
6.15 REQUESTING A CLUSTERED BAR CHART WITH CHART BUILDER ……………………………. 6-12
6.16 CLUSTERED BAR CHART FROM CHART BUILDER OUTPUT …………………………………….. 6-12
6.17 PROCEDURE: CLUSTERED BAR CHART WITH CHART BUILDER ………………………………. 6-13
6.18 EXAMPLE: CLUSTERED BAR CHART WITH CHART BUILDER ………………………………….. 6-15
6.19 ADDING A CONTROL VARIABLE …………………………………………………………………………. 6-16
6.20 REQUESTING A CONTROL VARIABLE ………………………………………………………………….. 6-17
6.21 CONTROL VARIABLE OUTPUT ……………………………………………………………………………. 6-17
6.22 PROCEDURE: ADDING A CONTROL VARIABLE ……………………………………………………… 6-18
6.23 EXAMPLE: ADDING A CONTROL VARIABLE …………………………………………………………. 6-19
6.24 EXTENSIONS: BEYOND CROSSTABS ……………………………………………………………………. 6-22
6.25 ASSOCIATION MEASURES………………………………………………………………………………….. 6-23
6.26 LESSON SUMMARY …………………………………………………………………………………………… 6-23
6.27 LEARNING ACTIVITY ………………………………………………………………………………………… 6-24
LESSON 7: THE INDEPENDENT- SAMPLES T TEST …………………….. 7-1
7.1 OBJECTIVES ………………………………………………………………………………………………………… 7-1
7.2 INTRODUCTION ……………………………………………………………………………………………………. 7-1
7.3 THE INDEPENDENT-SAMPLES T TEST …………………………………………………………………….. 7-1
7.4 INDEPENDENT-SAMPLES T TEST ASSUMPTIONS ………………………………………………………. 7-2
7.5 REQUESTING THE INDEPENDENT-SAMPLES T TEST ………………………………………………….. 7-2
7.6 INDEPENDENT-SAMPLES T TEST OUTPUT ……………………………………………………………….. 7-3
7.7 PROCEDURE: INDEPENDENT-SAMPLES T TEST ………………………………………………………… 7-5
7.8 DEMONSTRATION: INDEPENDENT-SAMPLES T TEST…………………………………………………. 7-6
7.9 ERROR BAR CHART ……………………………………………………………………………………………. 7-10
7.10 REQUESTING AN ERROR BAR CHART WITH CHART BUILDER …………………………………. 7-11
7.11 ERROR BAR CHART OUTPUT ……………………………………………………………………………… 7-11
7.12 DEMONSTRATION: ERROR BAR CHART WITH CHART BUILDER ……………………………… 7-12
7.13 LESSON SUMMARY …………………………………………………………………………………………… 7-14
7.14 LEARNING ACTIVITY ………………………………………………………………………………………… 7-14
LESSON 8: THE PAIRED-SAMPLES T TEST …………………………………. 8-1
iii
INTRODUCTION TO STATISTICAL ANALYSIS USING IBM SPSS STATISTICS
8.1 OBJECTIVES …………………………………………………………………………………………………………8-1
8.2 INTRODUCTION …………………………………………………………………………………………………….8-1
8.3 THE PAIRED-SAMPLES T TEST ………………………………………………………………………………..8-1
8.4 ASSUMPTIONS FOR THE PAIRED-SAMPLES T TEST …………………………………………………….8-2
8.5 REQUESTING A PAIRED-SAMPLES T TEST ………………………………………………………………..8-3
8.6 PAIRED-SAMPLES T TEST OUTPUT ………………………………………………………………………….8-3
8.7 PROCEDURE: PAIRED-SAMPLES T TEST……………………………………………………………………8-4
8.8 DEMONSTRATION: PAIRED-SAMPLES T TEST ……………………………………………………………8-4
8.9 LESSON SUMMARY ……………………………………………………………………………………………….8-6
8.10 LEARNING ACTIVITY …………………………………………………………………………………………..8-6
LESSON 9: ONE-WAY ANOVA ……………………………………………………… 9-1
9.1 OBJECTIVES …………………………………………………………………………………………………………9-1
9.2 INTRODUCTION …………………………………………………………………………………………………….9-1
9.3 ONE-WAY ANOVA ………………………………………………………………………………………………..9-1
9.4 ASSUMPTIONS OF ONE-WAY ANOVA …………………………………………………………………….9-2
9.5 REQUESTING ONE-WAY ANOVA …………………………………………………………………………..9-2
9.6 ONE-WAY ANOVA OUTPUT ………………………………………………………………………………….9-3
9.7 PROCEDURE: ONE-WAY ANOVA …………………………………………………………………………..9-4
9.8 DEMONSTRATION: ONE-WAY ANOVA …………………………………………………………………..9-6
9.9 POST HOC TESTS WITH A ONE-WAY ANOVA ………………………………………………………….9-8
9.10 REQUESTING POST HOC TESTS WITH A ONE-WAY ANOVA …………………………………….9-9
9.11 POST HOC TESTS OUTPUT…………………………………………………………………………………….9-9
9.12 PROCEDURE: POST HOC TESTS WITH A ONE-WAY ANOVA……………………………………9-10
9.13 DEMONSTRATION: POST HOC TESTS WITH A ONE-WAY ANOVA ……………………………9-12
9.14 ERROR BAR CHART WITH CHART BUILDER ………………………………………………………….9-14
9.15 REQUESTING AN ERROR BAR CHART WITH CHART BUILDER ………………………………….9-14
9.16 ERROR BAR CHART OUTPUT ………………………………………………………………………………9-14
9.17 PROCEDURE: ERROR BAR CHART WITH CHART BUILDER ……………………………………….9-15
9.18 DEMONSTRATION: ERROR BAR CHART WITH CHART BUILDER ……………………………….9-16
9.19 LESSON SUMMARY ……………………………………………………………………………………………9-18
9.20 LEARNING ACTIVITY …………………………………………………………………………………………9-18
LESSON 10: BIVARIATE PLOTS AND CORRELATIONS FOR SCALE
VARIABLES ……………………………………………………………………………… 10-1
10.1 OBJECTIVES ……………………………………………………………………………………………………..10-1
10.2 INTRODUCTION …………………………………………………………………………………………………10-1
10.3 SCATTERPLOTS …………………………………………………………………………………………………10-1
10.4 REQUESTING A SCATTERPLOT …………………………………………………………………………….10-2
10.5 SCATTERPLOT OUTPUT ………………………………………………………………………………………10-3
10.6 PROCEDURE: SCATTERPLOT ……………………………………………………………………………….10-3
10.7 DEMONSTRATION: SCATTERPLOT ………………………………………………………………………..10-4
10.8 ADDING A BEST FIT STRAIGHT LINE TO THE SCATTERPLOT ……………………………………10-5
10.9 PEARSON CORRELATION COEFFICIENT…………………………………………………………………10-7
10.10 REQUESTING A PEARSON CORRELATION COEFFICIENT…………………………………………10-8
10.11 BIVARIATE CORRELATION OUTPUT ……………………………………………………………………10-8
10.12 PROCEDURE: PEARSON CORRELATION WITH BIVARIATE CORRELATIONS ……………….10-9
10.13 DEMONSTRATION: PEARSON CORRELATION WITH BIVARIATE CORRELATIONS ……..10-10
10.14 LESSON SUMMARY ………………………………………………………………………………………..10-11
10.15 LEARNING ACTIVITY ……………………………………………………………………………………..10-12
iv
TABLE OF CONTENTS
LESSON 11: REGRESSION ANALYSIS………………………………………… 11-1
11.1 OBJECTIVES …………………………………………………………………………………………………….. 11-1
11.2 INTRODUCTION ………………………………………………………………………………………………… 11-1
11.3 SIMPLE LINEAR REGRESSION …………………………………………………………………………….. 11-1
11.4 SIMPLE LINEAR REGRESSION ASSUMPTIONS ……………………………………………………….. 11-3
11.5 REQUESTING SIMPLE LINEAR REGRESSION …………………………………………………………. 11-4
11.6 SIMPLE LINEAR REGRESSION OUTPUT ………………………………………………………………… 11-4
11.7 PROCEDURE: SIMPLE LINEAR REGRESSION …………………………………………………………. 11-5
11.8 DEMONSTRATION: SIMPLE LINEAR REGRESSION………………………………………………….. 11-7
11.9 MULTIPLE REGRESSION…………………………………………………………………………………… 11-11
11.10 MULTIPLE LINEAR REGRESSION ASSUMPTIONS ……………………………………………….. 11-11
11.11 REQUESTING MULTIPLE LINEAR REGRESSION………………………………………………….. 11-11
11.12 MULTIPLE LINEAR REGRESSION OUTPUT ………………………………………………………… 11-11
11.13 PROCEDURE: MULTIPLE LINEAR REGRESSION ………………………………………………….. 11-14
11.14 DEMONSTRATION: MULTIPLE LINEAR REGRESSION ………………………………………….. 11-16
11.15 LESSON SUMMARY ……………………………………………………………………………………….. 11-22
11.16 LEARNING ACTIVITY …………………………………………………………………………………….. 11-22
LESSON 12: NONPARAMETRIC TESTS ………………………………………. 12-1
12.1 OBJECTIVES …………………………………………………………………………………………………….. 12-1
12.2 INTRODUCTION ………………………………………………………………………………………………… 12-1
12.3 NONPARAMETRIC ANALYSES …………………………………………………………………………….. 12-2
12.4 THE INDEPENDENT SAMPLES NONPARAMETRIC ANALYSIS …………………………………… 12-2
12.5 REQUESTING AN INDEPENDENT SAMPLES NONPARAMETRIC ANALYSIS ………………….. 12-3
12.6 INDEPENDENT SAMPLES NONPARAMETRIC TESTS OUTPUT …………………………………… 12-3
12.7 PROCEDURE: INDEPENDENT SAMPLES NONPARAMETRIC TESTS …………………………….. 12-5
12.8 DEMONSTRATION: INDEPENDENT SAMPLES NONPARAMETRIC TESTS …………………….. 12-8
12.9 THE RELATED SAMPLES NONPARAMETRIC ANALYSIS ………………………………………… 12-11
12.10 REQUESTING A RELATED SAMPLES NONPARAMETRIC ANALYSIS ……………………….. 12-12
12.11 RELATED SAMPLES NONPARAMETRIC TESTS OUTPUT ………………………………………. 12-12
12.12 PROCEDURE: RELATED SAMPLES NONPARAMETRIC TESTS ……………………………….. 12-13
12.13 DEMONSTRATION: RELATED SAMPLES NONPARAMETRIC TESTS ………………………… 12-16
12.14 LESSON SUMMARY ……………………………………………………………………………………….. 12-19
12.15 LEARNING ACTIVITY …………………………………………………………………………………….. 12-20
LESSON 13: COURSE SUMMARY……………………………………………….. 13-1
13.1 COURSE OBJECTIVES REVIEW ……………………………………………………………………………. 13-1
13.2 COURSE REVIEW: DISCUSSION QUESTIONS …………………………………………………………. 13-1
13.3 NEXT STEPS …………………………………………………………………………………………………….. 13-2
APPENDIX A: INTRODUCTION TO STATISTICAL ANALYSIS
REFERENCES 1
1.1 INTRODUCTION …………………………………………………………………………………………………… A-1
1.2 REFERENCES ………………………………………………………………………………………………………. A-1
v
INTRODUCTION TO STATISTICAL ANALYSIS USING IBM SPSS STATISTICS
vi
COURSE INTRODUCTION
Lesson 0: Course Introduction
0.1 Introduction
®
The focus of this two-day course is an introduction to the statistical component of IBM
®
SPSS Statistics. This is an application-oriented course and the approach is practical. You’ll take a
look at several statistical techniques and discuss situations in which you would use each technique,
®
the assumptions made by each method, how to set up the analysis using PASW Statistics, as well
as how to interpret the results. This includes a broad range of techniques for exploring and
summarizing data, as well as investigating and testing underlying relationships. You will gain an
understanding of when and why to use these various techniques as well as how to apply them with
confidence, and interpret their output, and graphically display the results.
0.2 Course Objectives
After completing this course students will be able to:
•
Perform basic statistical analysis using selected statistical techniques with PASW Statistics
To support the achievement of this primary objective, students will also be able to:
• Explain the basic elements of quantitative research and issues that should be considered in
data analysis
• Determine the level of measurement of variables and obtain appropriate summary statistics
based on the level of measurement
• Run the Frequencies procedure to obtain appropriate summary statistics for categorical
variables
• Request and interpret appropriate summary statistics for scale variables
• Explain how to make inferences about populations from samples
• Perform crosstab analysis on categorical variables
• Perform a statistical test to determine whether there is a statistically significant relationship
between categorical variables
• Perform a statistical test to determine whether there is a statistically significant difference
between two groups on a scale variable
• Perform a statistical test to determine whether there is a statistically significant difference
between the means of two scale variables
• Perform a statistical test to determine whether there is a statistically significant difference
among three or more groups on a scale dependent variable
• Perform a statistical test to determine whether two scale variables are correlated (related)
• Perform linear regression to determine whether one or more variables can significantly
predict or explain a dependent variable
• Perform non-parametric tests on data that don’t meet the assumptions for standard statistical
tests
0.3 About SPSS
®
®
SPSS Inc., an IBM Company is a leading global provider of predictive analytics software and
solutions. The Company’s complete portfolio of products – data collection, statistics, modeling and
deployment – captures people’s attitudes and opinions, predicts outcomes of future customer
interactions, and then acts on these insights by embedding analytics into business processes. SPSS
solutions address interconnected business objectives across an entire organization by focusing on
the convergence of analytics, IT architecture and business process. Commercial, government and
academic customers worldwide rely on SPSS technology as a competitive advantage in attracting,
0-1
INTRODUCTION TO STATISTICAL ANALYSIS WITH IBM SPSS STATISTICS
retaining and growing customers, while reducing fraud and mitigating risk. SPSS was acquired by
®
IBM in October 2009. For more information, visit http://www.spss.com.
0.4 Supporting Materials
We use several datasets in the course because no one data file contains all the types of variables
and relationships between them that are ideal for every technique we discuss. As much as possible,
we try to minimize the need within one lesson to switch between datasets, but the first priority is to
use appropriate data for each method.
The following data files are used in this course:
• Bank.sav
• Drinks.sav
• Census.sav
• Employee data.sav
• SPSS_CUST.sav
0.5 Course Assumptions
General computer literacy. Completion of the “Introduction to PASW Statistics” and/or “Data
Management and Manipulation with PASW Statistics” courses or experience with PASW Statistics
including familiarity with, opening, defining, and saving data files and manipulating and saving output.
Basic statistical knowledge or at least one introductory level course in statistics is recommended.
Note about Default Startup Folder and Variable Display in Dialog Boxes
In this course, all of the files used for the demonstrations and exercises are located in the folder
c:TrainStatistics_IntroAnalysis.
Note: If the course files are stored in a different location, your instructor will give you instructions
specific to that location.
Either variable names or longer variable labels will appear in list boxes in dialog boxes. Additionally,
variables in list boxes can be ordered alphabetically or by their position in the file. In this course, we
will display variable names in alphabetical order within list boxes.
1)
2)
3)
4)
5)
0-2
Select Edit…Options
Select the General tab (if necessary)
Select Display names in the Variable Lists group on the General tab
Select Alphabetical
Select OK and OK in the information box to confirm the change
INTRODUCTION TO STATISTICAL ANALYSIS
Lesson 1: Introduction to Statistical
Analysis
1.1 Objectives
After completing this lesson students will be able to:
•
Explain the basic elements of quantitative research and issues that should be considered in
data analysis
To support the achievement of the primary objective, students will also be able to:
•
•
•
•
Explain the basic steps of the research process
Explain differences between populations and samples
Explain differences between experimental and non-experimental research designs
Explain differences between independent and dependent variables
1.2 Introduction
The goal of this course is to enable you to perform useful analyses on your data using PASW
Statistics. Keeping this in mind, these lessons demonstrate how to perform descriptive and inferential
statistical analyses and create charts to support these analyses. This course guide will focus on the
elements necessary for you to answer questions from your data.
In this chapter, we begin by briefly reviewing the basic elements of quantitative research and issues
that should be considered in data analysis. We will then discuss a number of statistical procedures
that PASW Statistics performs. This is an application-oriented course and the approach will be
practical. We will discuss:
1)
2)
3)
4)
The situations in which you would use each technique.
The assumptions made by the method.
How to set up the analysis using PASW Statistics.
Interpretation of the results.
We will not derive proofs, but rather focus on the practical matters of data analysis in support of
answering research questions. For example, we will discuss what correlation coefficients are, when to
use them, and how to produce and interpret them, but will not formally derive their properties. This
course is not a substitute for a course in statistics. You will benefit if you have had such a course in
the past, but even if not, you will understand the basics of each technique after completion of this
course.
We will cover descriptive statistics and exploratory data analysis, and then examine relationships
between categorical variables using crosstabulation tables and chi-square tests. Testing for mean
differences between groups using T Tests and analysis of variance (ANOVA) will be consider
Analysis Using IBM SPSS
Statistics
Student Guide
Course Code: 0G517
ERC 1.0
Introduction to Statistical Analysis Using IBM
SPSS Statistics
Licensed Materials – Property of IBM
© Copyright IBM Corp. 2010
0G517
Published October 2010
US Government Users Restricted Rights – Use,
duplication or disclosure restricted by GSA ADP
Schedule Contract with IBM Corp.
IBM, the IBM logo and ibm.com are trademarks
of International Business Machines Corp.,
registered in many jurisdictions worldwide.
SPSS, SamplePower, and PASW are trademarks
of SPSS Inc., an IBM Company, registered in
many jurisdictions worldwide.
Other product and service names might be
trademarks of IBM or other companies.
This guide contains proprietary information which
is protected by copyright. No part of this
document may be photocopied, reproduced, or
translated into another language without a legal
license agreement from IBM Corporation.
Any references in this information to non-IBM
Web sites are provided for convenience only and
do not in any manner serve as an endorsement
of those Web sites. The materials at those Web
sites are not part of the materials for this IBM
product and use of those Web sites is at your
own risk.
TABLE OF CONTENTS
Table of Contents
LESSON 0: COURSE INTRODUCTION ………………………………………….. 0-1
0.1 INTRODUCTION ……………………………………………………………………………………………………. 0-1
0.2 COURSE OBJECTIVES ……………………………………………………………………………………………. 0-1
0.3 ABOUT SPSS ………………………………………………………………………………………………………. 0-1
0.4 SUPPORTING MATERIALS ……………………………………………………………………………………… 0-2
0.5 COURSE ASSUMPTIONS ………………………………………………………………………………………… 0-2
LESSON 1: INTRODUCTION TO STATISTICAL ANALYSIS …………. 1-1
1.1 OBJECTIVES ………………………………………………………………………………………………………… 1-1
1.2 INTRODUCTION ……………………………………………………………………………………………………. 1-1
1.3 BASIC STEPS OF THE RESEARCH PROCESS ………………………………………………………………. 1-1
1.4 POPULATIONS AND SAMPLES ………………………………………………………………………………… 1-3
1.5 RESEARCH DESIGN ………………………………………………………………………………………………. 1-3
1.6 INDEPENDENT AND DEPENDENT VARIABLES …………………………………………………………… 1-4
1.7 NOTE ABOUT DEFAULT STARTUP FOLDER AND VARIABLE DISPLAY IN DIALOG BOXES .. 1-4
1.8 LESSON SUMMARY ………………………………………………………………………………………………. 1-5
1.9 LEARNING ACTIVITY ……………………………………………………………………………………………. 1-6
LESSON 2: UNDERSTANDING DATA DISTRIBUTIONS – THEORY2-1
2.1 OBJECTIVES ………………………………………………………………………………………………………… 2-1
INTRODUCTION …………………………………………………………………………………………………………. 2-1
2.2 LEVELS OF MEASUREMENT AND STATISTICAL METHODS …………………………………………. 2-1
2.3 MEASURES OF CENTRAL TENDENCY AND DISPERSION …………………………………………….. 2-5
2.4 NORMAL DISTRIBUTIONS ……………………………………………………………………………………… 2-7
2.5 STANDARDIZED (Z-) SCORES ………………………………………………………………………………… 2-8
2.6 REQUESTING STANDARDIZED (Z-) SCORES……………………………………………………………. 2-10
2.7 STANDARDIZED (Z-) SCORES OUTPUT ………………………………………………………………….. 2-10
2.8 PROCEDURE: DESCRIPTIVES FOR STANDARDIZED (Z-) SCORES ……………………………….. 2-10
2.9 DEMONSTRATION: DESCRIPTIVES FOR Z-SCORES…………………………………………………… 2-11
2.10 LESSON SUMMARY …………………………………………………………………………………………… 2-12
2.11 LEARNING ACTIVITY ………………………………………………………………………………………… 2-13
LESSON 3: DATA DISTRIBUTIONS FOR CATEGORICAL
VARIABLES ……………………………………………………………………………….. 3-1
3.1 OBJECTIVES ………………………………………………………………………………………………………… 3-1
3.2 INTRODUCTION ……………………………………………………………………………………………………. 3-1
3.3 USING FREQUENCIES TO SUMMARIZE NOMINAL AND ORDINAL VARIABLES ……………….. 3-2
3.4 REQUESTING FREQUENCIES ………………………………………………………………………………….. 3-3
3.5 FREQUENCIES OUTPUT …………………………………………………………………………………………. 3-3
3.6 PROCEDURE: FREQUENCIES ………………………………………………………………………………….. 3-4
3.7 DEMONSTRATION: FREQUENCIES …………………………………………………………………………… 3-6
3.8 LESSON SUMMARY …………………………………………………………………………………………….. 3-10
3.9 LEARNING ACTIVITY ………………………………………………………………………………………….. 3-10
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INTRODUCTION TO STATISTICAL ANALYSIS USING IBM SPSS STATISTICS
LESSON 4: DATA DISTRIBUTIONS FOR SCALE VARIABLES ……… 4-1
4.1 OBJECTIVES …………………………………………………………………………………………………………4-1
4.2 INTRODUCTION …………………………………………………………………………………………………….4-1
4.3 SUMMARIZING SCALE VARIABLES USING FREQUENCIES……………………………………………4-1
4.4 REQUESTING FREQUENCIES ……………………………………………………………………………………4-2
4.5 FREQUENCIES OUTPUT ………………………………………………………………………………………….4-2
4.6 PROCEDURE: FREQUENCIES ……………………………………………………………………………………4-4
4.7 DEMONSTRATION: FREQUENCIES ……………………………………………………………………………4-6
4.8 SUMMARIZING SCALE VARIABLES USING DESCRIPTIVES………………………………………….4-11
4.9 REQUESTING DESCRIPTIVES …………………………………………………………………………………4-11
4.10 DESCRIPTIVES OUTPUT ………………………………………………………………………………………4-11
4.11 PROCEDURE: DESCRIPTIVES ……………………………………………………………………………….4-11
4.12 DEMONSTRATION: DESCRIPTIVES………………………………………………………………………..4-12
4.13 SUMMARIZING SCALE VARIABLES USING THE EXPLORE PROCEDURE ………………………4-13
4.14 REQUESTING EXPLORE ………………………………………………………………………………………4-13
4.15 PROCEDURE: EXPLORE ………………………………………………………………………………………4-16
4.16 DEMONSTRATION: EXPLORE……………………………………………………………………………….4-19
4.17 LESSON SUMMARY ……………………………………………………………………………………………4-24
4.18 LEARNING ACTIVITY …………………………………………………………………………………………4-25
LESSON 5: MAKING INFERENCES ABOUT POPULATIONS FROM
SAMPLES
……………………………………………………………………………….. 5-1
5.1 OBJECTIVES …………………………………………………………………………………………………………5-1
5.2 INTRODUCTION …………………………………………………………………………………………………….5-1
5.3 BASICS OF MAKING INFERENCES ABOUT POPULATIONS FROM SAMPLES ……………………..5-1
5.4 INFLUENCE OF SAMPLE SIZE …………………………………………………………………………………..5-2
5.5 HYPOTHESIS TESTING ………………………………………………………………………………………….5-10
5.6 THE NATURE OF PROBABILITY ……………………………………………………………………………..5-11
5.7 TYPES OF STATISTICAL ERRORS ……………………………………………………………………………5-11
5.8 STATISTICAL SIGNIFICANCE AND PRACTICAL IMPORTANCE ……………………………………..5-12
5.9 LESSON SUMMARY ……………………………………………………………………………………………..5-13
5.10 LEARNING ACTIVITY …………………………………………………………………………………………5-13
LESSON 6: RELATIONSHIPS BETWEEN CATEGORICAL
VARIABLES ………………………………………………………………………………. 6-1
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TABLE OF CONTENTS
6.1 OBJECTIVES ………………………………………………………………………………………………………… 6-1
6.2 INTRODUCTION ……………………………………………………………………………………………………. 6-1
6.3 CROSSTABS…………………………………………………………………………………………………………. 6-2
6.4 CROSSTABS ASSUMPTIONS……………………………………………………………………………………. 6-3
6.5 REQUESTING CROSSTABS ……………………………………………………………………………………… 6-3
6.6 CROSSTABS OUTPUT ……………………………………………………………………………………………. 6-3
6.7 PROCEDURE: CROSSTABS ……………………………………………………………………………………… 6-4
6.8 EXAMPLE: CROSSTABS …………………………………………………………………………………………. 6-5
6.9 CHI-SQUARE TEST ……………………………………………………………………………………………….. 6-7
6.10 REQUESTING THE CHI-SQUARE TEST ……………………………………………………………………. 6-8
6.11 CHI-SQUARE OUTPUT…………………………………………………………………………………………. 6-8
6.12 PROCEDURE: CHI-SQUARE TEST ………………………………………………………………………….. 6-9
6.13 EXAMPLE: CHI-SQUARE TEST ……………………………………………………………………………. 6-10
6.14 CLUSTERED BAR CHART …………………………………………………………………………………… 6-11
6.15 REQUESTING A CLUSTERED BAR CHART WITH CHART BUILDER ……………………………. 6-12
6.16 CLUSTERED BAR CHART FROM CHART BUILDER OUTPUT …………………………………….. 6-12
6.17 PROCEDURE: CLUSTERED BAR CHART WITH CHART BUILDER ………………………………. 6-13
6.18 EXAMPLE: CLUSTERED BAR CHART WITH CHART BUILDER ………………………………….. 6-15
6.19 ADDING A CONTROL VARIABLE …………………………………………………………………………. 6-16
6.20 REQUESTING A CONTROL VARIABLE ………………………………………………………………….. 6-17
6.21 CONTROL VARIABLE OUTPUT ……………………………………………………………………………. 6-17
6.22 PROCEDURE: ADDING A CONTROL VARIABLE ……………………………………………………… 6-18
6.23 EXAMPLE: ADDING A CONTROL VARIABLE …………………………………………………………. 6-19
6.24 EXTENSIONS: BEYOND CROSSTABS ……………………………………………………………………. 6-22
6.25 ASSOCIATION MEASURES………………………………………………………………………………….. 6-23
6.26 LESSON SUMMARY …………………………………………………………………………………………… 6-23
6.27 LEARNING ACTIVITY ………………………………………………………………………………………… 6-24
LESSON 7: THE INDEPENDENT- SAMPLES T TEST …………………….. 7-1
7.1 OBJECTIVES ………………………………………………………………………………………………………… 7-1
7.2 INTRODUCTION ……………………………………………………………………………………………………. 7-1
7.3 THE INDEPENDENT-SAMPLES T TEST …………………………………………………………………….. 7-1
7.4 INDEPENDENT-SAMPLES T TEST ASSUMPTIONS ………………………………………………………. 7-2
7.5 REQUESTING THE INDEPENDENT-SAMPLES T TEST ………………………………………………….. 7-2
7.6 INDEPENDENT-SAMPLES T TEST OUTPUT ……………………………………………………………….. 7-3
7.7 PROCEDURE: INDEPENDENT-SAMPLES T TEST ………………………………………………………… 7-5
7.8 DEMONSTRATION: INDEPENDENT-SAMPLES T TEST…………………………………………………. 7-6
7.9 ERROR BAR CHART ……………………………………………………………………………………………. 7-10
7.10 REQUESTING AN ERROR BAR CHART WITH CHART BUILDER …………………………………. 7-11
7.11 ERROR BAR CHART OUTPUT ……………………………………………………………………………… 7-11
7.12 DEMONSTRATION: ERROR BAR CHART WITH CHART BUILDER ……………………………… 7-12
7.13 LESSON SUMMARY …………………………………………………………………………………………… 7-14
7.14 LEARNING ACTIVITY ………………………………………………………………………………………… 7-14
LESSON 8: THE PAIRED-SAMPLES T TEST …………………………………. 8-1
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INTRODUCTION TO STATISTICAL ANALYSIS USING IBM SPSS STATISTICS
8.1 OBJECTIVES …………………………………………………………………………………………………………8-1
8.2 INTRODUCTION …………………………………………………………………………………………………….8-1
8.3 THE PAIRED-SAMPLES T TEST ………………………………………………………………………………..8-1
8.4 ASSUMPTIONS FOR THE PAIRED-SAMPLES T TEST …………………………………………………….8-2
8.5 REQUESTING A PAIRED-SAMPLES T TEST ………………………………………………………………..8-3
8.6 PAIRED-SAMPLES T TEST OUTPUT ………………………………………………………………………….8-3
8.7 PROCEDURE: PAIRED-SAMPLES T TEST……………………………………………………………………8-4
8.8 DEMONSTRATION: PAIRED-SAMPLES T TEST ……………………………………………………………8-4
8.9 LESSON SUMMARY ……………………………………………………………………………………………….8-6
8.10 LEARNING ACTIVITY …………………………………………………………………………………………..8-6
LESSON 9: ONE-WAY ANOVA ……………………………………………………… 9-1
9.1 OBJECTIVES …………………………………………………………………………………………………………9-1
9.2 INTRODUCTION …………………………………………………………………………………………………….9-1
9.3 ONE-WAY ANOVA ………………………………………………………………………………………………..9-1
9.4 ASSUMPTIONS OF ONE-WAY ANOVA …………………………………………………………………….9-2
9.5 REQUESTING ONE-WAY ANOVA …………………………………………………………………………..9-2
9.6 ONE-WAY ANOVA OUTPUT ………………………………………………………………………………….9-3
9.7 PROCEDURE: ONE-WAY ANOVA …………………………………………………………………………..9-4
9.8 DEMONSTRATION: ONE-WAY ANOVA …………………………………………………………………..9-6
9.9 POST HOC TESTS WITH A ONE-WAY ANOVA ………………………………………………………….9-8
9.10 REQUESTING POST HOC TESTS WITH A ONE-WAY ANOVA …………………………………….9-9
9.11 POST HOC TESTS OUTPUT…………………………………………………………………………………….9-9
9.12 PROCEDURE: POST HOC TESTS WITH A ONE-WAY ANOVA……………………………………9-10
9.13 DEMONSTRATION: POST HOC TESTS WITH A ONE-WAY ANOVA ……………………………9-12
9.14 ERROR BAR CHART WITH CHART BUILDER ………………………………………………………….9-14
9.15 REQUESTING AN ERROR BAR CHART WITH CHART BUILDER ………………………………….9-14
9.16 ERROR BAR CHART OUTPUT ………………………………………………………………………………9-14
9.17 PROCEDURE: ERROR BAR CHART WITH CHART BUILDER ……………………………………….9-15
9.18 DEMONSTRATION: ERROR BAR CHART WITH CHART BUILDER ……………………………….9-16
9.19 LESSON SUMMARY ……………………………………………………………………………………………9-18
9.20 LEARNING ACTIVITY …………………………………………………………………………………………9-18
LESSON 10: BIVARIATE PLOTS AND CORRELATIONS FOR SCALE
VARIABLES ……………………………………………………………………………… 10-1
10.1 OBJECTIVES ……………………………………………………………………………………………………..10-1
10.2 INTRODUCTION …………………………………………………………………………………………………10-1
10.3 SCATTERPLOTS …………………………………………………………………………………………………10-1
10.4 REQUESTING A SCATTERPLOT …………………………………………………………………………….10-2
10.5 SCATTERPLOT OUTPUT ………………………………………………………………………………………10-3
10.6 PROCEDURE: SCATTERPLOT ……………………………………………………………………………….10-3
10.7 DEMONSTRATION: SCATTERPLOT ………………………………………………………………………..10-4
10.8 ADDING A BEST FIT STRAIGHT LINE TO THE SCATTERPLOT ……………………………………10-5
10.9 PEARSON CORRELATION COEFFICIENT…………………………………………………………………10-7
10.10 REQUESTING A PEARSON CORRELATION COEFFICIENT…………………………………………10-8
10.11 BIVARIATE CORRELATION OUTPUT ……………………………………………………………………10-8
10.12 PROCEDURE: PEARSON CORRELATION WITH BIVARIATE CORRELATIONS ……………….10-9
10.13 DEMONSTRATION: PEARSON CORRELATION WITH BIVARIATE CORRELATIONS ……..10-10
10.14 LESSON SUMMARY ………………………………………………………………………………………..10-11
10.15 LEARNING ACTIVITY ……………………………………………………………………………………..10-12
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TABLE OF CONTENTS
LESSON 11: REGRESSION ANALYSIS………………………………………… 11-1
11.1 OBJECTIVES …………………………………………………………………………………………………….. 11-1
11.2 INTRODUCTION ………………………………………………………………………………………………… 11-1
11.3 SIMPLE LINEAR REGRESSION …………………………………………………………………………….. 11-1
11.4 SIMPLE LINEAR REGRESSION ASSUMPTIONS ……………………………………………………….. 11-3
11.5 REQUESTING SIMPLE LINEAR REGRESSION …………………………………………………………. 11-4
11.6 SIMPLE LINEAR REGRESSION OUTPUT ………………………………………………………………… 11-4
11.7 PROCEDURE: SIMPLE LINEAR REGRESSION …………………………………………………………. 11-5
11.8 DEMONSTRATION: SIMPLE LINEAR REGRESSION………………………………………………….. 11-7
11.9 MULTIPLE REGRESSION…………………………………………………………………………………… 11-11
11.10 MULTIPLE LINEAR REGRESSION ASSUMPTIONS ……………………………………………….. 11-11
11.11 REQUESTING MULTIPLE LINEAR REGRESSION………………………………………………….. 11-11
11.12 MULTIPLE LINEAR REGRESSION OUTPUT ………………………………………………………… 11-11
11.13 PROCEDURE: MULTIPLE LINEAR REGRESSION ………………………………………………….. 11-14
11.14 DEMONSTRATION: MULTIPLE LINEAR REGRESSION ………………………………………….. 11-16
11.15 LESSON SUMMARY ……………………………………………………………………………………….. 11-22
11.16 LEARNING ACTIVITY …………………………………………………………………………………….. 11-22
LESSON 12: NONPARAMETRIC TESTS ………………………………………. 12-1
12.1 OBJECTIVES …………………………………………………………………………………………………….. 12-1
12.2 INTRODUCTION ………………………………………………………………………………………………… 12-1
12.3 NONPARAMETRIC ANALYSES …………………………………………………………………………….. 12-2
12.4 THE INDEPENDENT SAMPLES NONPARAMETRIC ANALYSIS …………………………………… 12-2
12.5 REQUESTING AN INDEPENDENT SAMPLES NONPARAMETRIC ANALYSIS ………………….. 12-3
12.6 INDEPENDENT SAMPLES NONPARAMETRIC TESTS OUTPUT …………………………………… 12-3
12.7 PROCEDURE: INDEPENDENT SAMPLES NONPARAMETRIC TESTS …………………………….. 12-5
12.8 DEMONSTRATION: INDEPENDENT SAMPLES NONPARAMETRIC TESTS …………………….. 12-8
12.9 THE RELATED SAMPLES NONPARAMETRIC ANALYSIS ………………………………………… 12-11
12.10 REQUESTING A RELATED SAMPLES NONPARAMETRIC ANALYSIS ……………………….. 12-12
12.11 RELATED SAMPLES NONPARAMETRIC TESTS OUTPUT ………………………………………. 12-12
12.12 PROCEDURE: RELATED SAMPLES NONPARAMETRIC TESTS ……………………………….. 12-13
12.13 DEMONSTRATION: RELATED SAMPLES NONPARAMETRIC TESTS ………………………… 12-16
12.14 LESSON SUMMARY ……………………………………………………………………………………….. 12-19
12.15 LEARNING ACTIVITY …………………………………………………………………………………….. 12-20
LESSON 13: COURSE SUMMARY……………………………………………….. 13-1
13.1 COURSE OBJECTIVES REVIEW ……………………………………………………………………………. 13-1
13.2 COURSE REVIEW: DISCUSSION QUESTIONS …………………………………………………………. 13-1
13.3 NEXT STEPS …………………………………………………………………………………………………….. 13-2
APPENDIX A: INTRODUCTION TO STATISTICAL ANALYSIS
REFERENCES 1
1.1 INTRODUCTION …………………………………………………………………………………………………… A-1
1.2 REFERENCES ………………………………………………………………………………………………………. A-1
v
INTRODUCTION TO STATISTICAL ANALYSIS USING IBM SPSS STATISTICS
vi
COURSE INTRODUCTION
Lesson 0: Course Introduction
0.1 Introduction
®
The focus of this two-day course is an introduction to the statistical component of IBM
®
SPSS Statistics. This is an application-oriented course and the approach is practical. You’ll take a
look at several statistical techniques and discuss situations in which you would use each technique,
®
the assumptions made by each method, how to set up the analysis using PASW Statistics, as well
as how to interpret the results. This includes a broad range of techniques for exploring and
summarizing data, as well as investigating and testing underlying relationships. You will gain an
understanding of when and why to use these various techniques as well as how to apply them with
confidence, and interpret their output, and graphically display the results.
0.2 Course Objectives
After completing this course students will be able to:
•
Perform basic statistical analysis using selected statistical techniques with PASW Statistics
To support the achievement of this primary objective, students will also be able to:
• Explain the basic elements of quantitative research and issues that should be considered in
data analysis
• Determine the level of measurement of variables and obtain appropriate summary statistics
based on the level of measurement
• Run the Frequencies procedure to obtain appropriate summary statistics for categorical
variables
• Request and interpret appropriate summary statistics for scale variables
• Explain how to make inferences about populations from samples
• Perform crosstab analysis on categorical variables
• Perform a statistical test to determine whether there is a statistically significant relationship
between categorical variables
• Perform a statistical test to determine whether there is a statistically significant difference
between two groups on a scale variable
• Perform a statistical test to determine whether there is a statistically significant difference
between the means of two scale variables
• Perform a statistical test to determine whether there is a statistically significant difference
among three or more groups on a scale dependent variable
• Perform a statistical test to determine whether two scale variables are correlated (related)
• Perform linear regression to determine whether one or more variables can significantly
predict or explain a dependent variable
• Perform non-parametric tests on data that don’t meet the assumptions for standard statistical
tests
0.3 About SPSS
®
®
SPSS Inc., an IBM Company is a leading global provider of predictive analytics software and
solutions. The Company’s complete portfolio of products – data collection, statistics, modeling and
deployment – captures people’s attitudes and opinions, predicts outcomes of future customer
interactions, and then acts on these insights by embedding analytics into business processes. SPSS
solutions address interconnected business objectives across an entire organization by focusing on
the convergence of analytics, IT architecture and business process. Commercial, government and
academic customers worldwide rely on SPSS technology as a competitive advantage in attracting,
0-1
INTRODUCTION TO STATISTICAL ANALYSIS WITH IBM SPSS STATISTICS
retaining and growing customers, while reducing fraud and mitigating risk. SPSS was acquired by
®
IBM in October 2009. For more information, visit http://www.spss.com.
0.4 Supporting Materials
We use several datasets in the course because no one data file contains all the types of variables
and relationships between them that are ideal for every technique we discuss. As much as possible,
we try to minimize the need within one lesson to switch between datasets, but the first priority is to
use appropriate data for each method.
The following data files are used in this course:
• Bank.sav
• Drinks.sav
• Census.sav
• Employee data.sav
• SPSS_CUST.sav
0.5 Course Assumptions
General computer literacy. Completion of the “Introduction to PASW Statistics” and/or “Data
Management and Manipulation with PASW Statistics” courses or experience with PASW Statistics
including familiarity with, opening, defining, and saving data files and manipulating and saving output.
Basic statistical knowledge or at least one introductory level course in statistics is recommended.
Note about Default Startup Folder and Variable Display in Dialog Boxes
In this course, all of the files used for the demonstrations and exercises are located in the folder
c:TrainStatistics_IntroAnalysis.
Note: If the course files are stored in a different location, your instructor will give you instructions
specific to that location.
Either variable names or longer variable labels will appear in list boxes in dialog boxes. Additionally,
variables in list boxes can be ordered alphabetically or by their position in the file. In this course, we
will display variable names in alphabetical order within list boxes.
1)
2)
3)
4)
5)
0-2
Select Edit…Options
Select the General tab (if necessary)
Select Display names in the Variable Lists group on the General tab
Select Alphabetical
Select OK and OK in the information box to confirm the change
INTRODUCTION TO STATISTICAL ANALYSIS
Lesson 1: Introduction to Statistical
Analysis
1.1 Objectives
After completing this lesson students will be able to:
•
Explain the basic elements of quantitative research and issues that should be considered in
data analysis
To support the achievement of the primary objective, students will also be able to:
•
•
•
•
Explain the basic steps of the research process
Explain differences between populations and samples
Explain differences between experimental and non-experimental research designs
Explain differences between independent and dependent variables
1.2 Introduction
The goal of this course is to enable you to perform useful analyses on your data using PASW
Statistics. Keeping this in mind, these lessons demonstrate how to perform descriptive and inferential
statistical analyses and create charts to support these analyses. This course guide will focus on the
elements necessary for you to answer questions from your data.
In this chapter, we begin by briefly reviewing the basic elements of quantitative research and issues
that should be considered in data analysis. We will then discuss a number of statistical procedures
that PASW Statistics performs. This is an application-oriented course and the approach will be
practical. We will discuss:
1)
2)
3)
4)
The situations in which you would use each technique.
The assumptions made by the method.
How to set up the analysis using PASW Statistics.
Interpretation of the results.
We will not derive proofs, but rather focus on the practical matters of data analysis in support of
answering research questions. For example, we will discuss what correlation coefficients are, when to
use them, and how to produce and interpret them, but will not formally derive their properties. This
course is not a substitute for a course in statistics. You will benefit if you have had such a course in
the past, but even if not, you will understand the basics of each technique after completion of this
course.
We will cover descriptive statistics and exploratory data analysis, and then examine relationships
between categorical variables using crosstabulation tables and chi-square tests. Testing for mean
differences between groups using T Tests and analysis of variance (ANOVA) will be consider
Categories: